Explore Workflows
View already parsed workflows here or click here to add your own
Graph | Name | Retrieved From | View |
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format_rrnas_from_seq_entry
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https://github.com/ncbi/pgap.git
Path: task_types/tt_format_rrnas_from_seq_entry.cwl Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7 |
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tt_kmer_compare_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_compare_wnode.cwl Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7 |
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allele-process-strain.cwl
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https://github.com/datirium/workflows.git
Path: subworkflows/allele-process-strain.cwl Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c |
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tt_blastn_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_blastn_wnode.cwl Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8 |
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GSEApy - Gene Set Enrichment Analysis in Python
GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html |
https://github.com/datirium/workflows.git
Path: workflows/gseapy.cwl Branch/Commit ID: 27bee2c853c98af5ce8ace0585b74658adc2e955 |
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Detect Variants workflow
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https://github.com/genome/analysis-workflows.git
Path: definitions/pipelines/detect_variants_mouse.cwl Branch/Commit ID: 449bc7e45bb02316d040f73838ef18359e770268 |
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heatmap-prepare.cwl
Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order. |
https://github.com/datirium/workflows.git
Path: subworkflows/heatmap-prepare.cwl Branch/Commit ID: bfa3843bcf36125ff258d6314f64b41336f06e6b |
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Indices builder from GBOL RDF (TTL)
Workflow to build different indices for different tools from a genome and transcriptome. This workflow expects an (annotated) genome in GBOL ttl format. Steps: - SAPP: rdf2gtf (genome fasta) - SAPP: rdf2fasta (transcripts fasta) - STAR index (Optional for Eukaryotic origin) - bowtie2 index - kallisto index |
https://git.wageningenur.nl/unlock/cwl.git
Path: cwl/workflows/workflow_indexbuilder.cwl Branch/Commit ID: d6893a25b58b9b25fb76c5e060974b54d9eabc41 |
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RNA-Seq pipeline single-read stranded mitochondrial
Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `samtools sort` to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using `GEEP` reads-counting utility; export results to file |
https://github.com/datirium/workflows.git
Path: workflows/rnaseq-se-dutp-mitochondrial.cwl Branch/Commit ID: 4106b7dc96e968db291b7a61ecd1641aa3b3dd6d |
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xenbase-sra-to-fastq-se.cwl
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https://github.com/datirium/workflows.git
Path: subworkflows/xenbase-sra-to-fastq-se.cwl Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c |